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Graduation science and technology english writing Presentation.pptx
- 1. Application of ArtificialIntelligence and Machine Learning in the Field of Materials Science Exploring AI-driven advancements in material discovery and development Name: Hasan Galiv （韩篙夫） Student I’d: 22430010519 Professor Name: Zhao Hongyang
- 2. Introduction to Materials Science Title: What is Materials Science? • Definition: Study of the properties and applications of materials, including metals, ceramics, polymers, and composites. • Importance: Materials science plays a critical role in engineering, electronics, and industrial development. • Limitations: Traditional materials discovery is time-consuming and expensive.
- 3. Role of ArtificialIntelligence in Materials Science How AI is Transforming Materials Science • AI and machine learning (ML) allow faster discovery of new materials by predicting properties and performance. • AI-driven simulations and modeling help reduce the need for physical experiments. • AI in microscopy, crystallography, and phase identification.
- 4. Machine Learning Applicationsin Materials Discovery Machine Learning for Predicting Material Properties • Supervised and unsupervised learning methods help analyze vast datasets. • ML models predict thermal, electronic, and mechanical properties of materials. • Example: Predicting battery materials with ML to optimize energy storage performance.
- 5. AI in MaterialDesign and Optimization Designing and Optimizing Materials with AI • AI helps optimize materials for specific properties, such as strength, conductivity, and flexibility. • Use cases: Designing lighter, stronger alloys for aerospace and new polymers for medical devices. • Automated experimentation: AI- driven labs that perform and interpret experiments.
- 6. Ambition of AIand ML in Materials Science Revolutionizing Material Discovery: • AI and ML significantly speed up the discovery of new materials by predicting properties and outcomes. • Enables exploration of complex material combinations, leading to advanced materials with optimized properties. Sustainability and Efficiency: • AI-driven materials design promotes the development of more sustainable and eco-friendly materials. • Optimizes resource usage and minimizes waste in production processes. Accelerating Research and Innovation: • Reduces the need for exhaustive trial- and-error experiments. • Enhances understanding of structure- property relationships in materials, aiding in faster innovation cycles. Goal: Leveraging AI and ML to transform materials science into a data-driven, highly efficient field with potential breakthroughs in technology and sustainability.
- 7. Planning for AIand ML Integration in Materials Science Data Collection and Management: • Building comprehensive databases of material properties and experimental data. • Integration of high-throughput experimentation and AI to generate new datasets for ML training. Development of AI Models: • Designing predictive models based on existing data to forecast material properties and performance. • Implementing ML algorithms for anomaly detection and optimization in material synthesis. Industry Collaboration and Cross- Disciplinary Research: • Collaboration between materials scientists, computer scientists, and industry experts for effective AI/ML implementation. • Fostering interdisciplinary research to bridge the gap between theoretical models and practical applications. Real-World Application and Scaling: • Developing AI models that can be used in industrial-scale material production. • Applying AI to enhance material testing, ensuring reliability and consistency.
- 8. Future Trends andConclusion The Future of AI in Materials Science • AI will play an even larger role in accelerating materials innovation. • Cross-disciplinary collaboration will become critical. • Challenges: Data quality, computational limits, and integration into traditional practices.
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